QUALITATIVE HIGH FIELD STRUCTURAL IMAGE ANALYSIS

定性高场结构图像分析

• 批准号: 7957227
• 负责人: Colin Studholme
• 金额: $ 7.69万
• 依托单位: NORTHERN CALIFORNIA INSTITUTE/RES/EDU
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7957227
• 关键词: Adult; Aging; Brain; Computer Retrieval of Information on Scientific Projects Database; Data; Development; Entropy; Funding; Grant; Histocompatibility Testing; Image; Image Analysis; Institution; Internet; Location; Magnetic Resonance Imaging; Maps; Methods; Motion; Neurodegenerative Disorders; Probability; Publishing; Research; Research Personnel; Resolution; Resources; Source; Tissues; United States National Institutes of Health; Weight; Work; base; image registration; improved; morphometry; novel; population based; reconstruction

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The overall aim of this project is to develop and build on our earlier funded work on the segmentation and spatial normalisation for regional morphometry at 1.5T, to make use of novel new imaging contrasts becoming available in higher field imaging. Aim 1: Multi-Sequence Fusion and High Resolution Tissue Segmentation: Development of automated tissue segmentation methods which estimate a voxel by voxel map of both the fractional occupancy and the probability of tissue types from multi-acquisition variable T1 weighted imaging data. This willincorporate inter-frame co-alignment, motion frame rejection, intensity inhomogeneity correction of the frames, and co-alignment and fusion of this data with other MRI sequences (such as DTI and FLAIR) acquired in a typical study. Aim 2: Improved Whole Brain Spatial Normalisation: Develop and refine multi-sequence fine-scalespatial normalisation methods for novel image contrasts, which make use of multiple-contrasts at high isotropic resolution. This will employ large scale diffeomorphic and symmetric image registration driven by robust regionalized entropy based registration criteria. We will also construct and use a high-resolution target template for spatial normalisation. Aim 3: High Resolution Map of T1 values in the Aging Adult Brain: Construction of a population based statistical map capturing the expected T1 values and their variance with location over a set of normal control subjects using the tissue segmentation and spatial normalisation methods developed in aims 1 and 2, together with the acquisition and reconstruction methods developed in this resource proposal. This statistical map will be published on the web as a resource for other researchers to make use of.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 该项目的总体目标是开发和建立我们早期资助的 1.5T 区域形态测量的分割和空间归一化工作，以利用在更高视场成像中可用的新颖的新成像对比度。 目标 1：多序列融合和高分辨率组织分割：开发自动组织分割方法，根据多采集变量 T1 加权成像数据逐个体素图估计分数占用率和组织类型的概率。这将结合帧间协同对准、运动帧抑制、强度不均匀性校正 帧，并将该数据与典型研究中获得的其他 MRI 序列（例如 DTI 和 FLAIR）进行联合对齐和融合。 目标 2：改进全脑空间标准化：开发和完善用于新颖图像对比度的多序列精细尺度空间标准化方法，该方法利用高各向同性分辨率下的多重对比度。这将采用由鲁棒的基于区域熵的配准标准驱动的大规模微分同胚和对称图像配准。我们还将构建并使用高分辨率 空间标准化的目标模板。 目标 3：老龄化成人大脑中 T1 值的高分辨率图：使用目标中开发的组织分割和空间归一化方法，构建基于人群的统计图，捕获预期 T1 值及其与一组正常对照受试者的位置的方差1 和 2，以及本资源提案中制定的获取和重建方法。该统计图将发布在网络上，作为其他研究人员使用的资源。